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Doctoral thesis
Open access
English

Measures of model adequacy and model selection in mixed-effects models

ContributorsJacot, Nadège
Defense date2016-09-13
Abstract

This thesis contributes to the development of measures of model selection and model adequacy for mixed-effects models. In the context of linear mixed-effects models, we review and compare in a simulation study a large set of measures proposed to evaluate model adequacy or/and to perform model selection. In the more general context of generalized linear mixed-effects models, we develop a measure of both model adequacy and model selection, that we name PRDpen. As a measure of model adequacy, our proposition gives information about the model at hand, as it measures the proportional reduction in deviance due to the model of interest in comparison with a prespecified null model. Furthermore, as a measure of model selection, PRDpen is able to choose the model that best fits the data among a set of alternatives, similarly to the information criteria.

eng
Citation (ISO format)
JACOT, Nadège. Measures of model adequacy and model selection in mixed-effects models. 2016. doi: 10.13097/archive-ouverte/unige:90527
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Technical informations

Creation11/16/2016 5:20:00 PM
First validation11/16/2016 5:20:00 PM
Update time03/15/2023 1:12:36 AM
Status update03/15/2023 1:12:35 AM
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